TensorFlow vs Datatron Comparison | StackShare | StackShare

Datatron vs TensorFlow

Overview

TensorFlow

Stacks3.8K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

Datatron

Stacks0

Followers10

Votes0

TensorFlow vs Datatron: What are the differences?

TensorFlow: Open Source Software Library for Machine Intelligence. TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API; Datatron: Production AI Model Management at Scale. Automate the standardized deployment, monitoring, governance, and validation of all your models to be developed in any environment.

TensorFlow and Datatron can be categorized as "Machine Learning" tools.

TensorFlow is an open source tool with 141K GitHub stars and 79.7K GitHub forks. Here's a link to TensorFlow's open source repository on GitHub.

Advice on TensorFlow, Datatron

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

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Comments

Detailed Comparison

TensorFlow	Datatron
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.	Automate the standardized deployment, monitoring, governance, and validation of all your models to be developed in any environment.
-	Explore models built and uploaded by your Data Science team, all from one centralized repository; Create and scale model deployments in just a few clicks. Deploy models developed in any framework or language; Make better business decisions to save your team time and money. Monitor model performance and detect model decay as it happens; Spend less time on model validation, bias detection, and internal audit processes. Go from model development to internal auditing to production faster than ever; Manage multivariate models through A/B testing for live inference and batch tasks; Apply business logic to your model prediction results. Create workflows for your models using multiple sources and languages
Statistics
GitHub Stars 192.3K	GitHub Stars -
GitHub Forks 74.9K	GitHub Forks -
Stacks 3.8K	Stacks 0
Followers 3.5K	Followers 10
Votes 106	Votes 0
Pros & Cons
Pros 32 High Performance 19 Connect Research and Production 16 Deep Flexibility 12 Auto-Differentiation 11 True Portability Cons 9 Hard 6 Hard to debug 2 Documentation not very helpful	No community feedback yet
Integrations
JavaScript	scikit-learn H2O

What are some alternatives to TensorFlow, Datatron?

Heroku

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Clever Cloud

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Google App Engine

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Red Hat OpenShift

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AWS Elastic Beanstalk

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Render

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Hasura

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Cloud 66

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Jelastic

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Dokku

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